
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "55"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 102 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 98 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_maintenance	100.00%	13.51%	100.00%	0.00%	100.00%	0.00%	0.710991	11.043790	1.130717	81.610373	4.696874	27.487029	3.945286	28.542670	0.5610	0.1320	0.2142	8.157318	1.279068
2459593	RF_maintenance	100.00%	48.14%	100.00%	0.00%	100.00%	0.00%	0.472856	18.897082	2.496580	86.113779	5.760062	37.762668	2.635337	11.673192	0.4980	0.1238	0.2008	7.016051	1.258722
2459592	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	0.905762	1.483953	0.618472	7.660803	-0.375646	1.868923	-1.913806	0.850891	0.0358	0.0285	0.0009	nan	nan
2459591	RF_maintenance	100.00%	94.59%	100.00%	0.00%	100.00%	0.00%	0.412495	22.163453	3.054475	100.023799	4.691826	32.425807	-0.962719	3.665744	0.3113	0.0726	0.0763	7.640439	1.312706
2459590	RF_maintenance	100.00%	94.59%	100.00%	0.00%	100.00%	0.00%	0.643117	25.505548	2.751541	93.209927	4.968009	34.903000	-0.664863	5.950902	0.3113	0.0752	0.0725	5.818332	1.291577
2459589	RF_maintenance	100.00%	56.73%	94.06%	0.00%	100.00%	0.00%	1.245273	23.351068	1.894440	86.374141	7.017160	22.032671	5.485180	17.267733	0.4542	0.1223	0.1298	4.588263	1.248984
2459588	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	223.057335	222.282936	inf	inf	23.222302	26.647573	20.302649	18.610535	nan	nan	nan	0.000000	0.000000
2459587	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	0.372667	23.247504	10.979985	134.838791	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	5.732558	0.000000
2459586	RF_maintenance	100.00%	81.62%	100.00%	0.00%	100.00%	0.00%	-0.362310	20.140787	0.579208	31.217399	0.503432	11.412535	-0.511896	7.118390	0.3336	0.0758	0.0797	3.844712	1.259690
2459585	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.571187	25.128928	2.697037	9.809859	0.537563	10.178971	-1.526605	4.258584	0.0386	0.0290	0.0028	0.000000	0.000000
2459584	RF_maintenance	100.00%	95.80%	100.00%	0.00%	100.00%	0.00%	4.093157	27.870660	7.271273	115.551803	18.908964	18.739977	28.010143	30.585003	0.2813	0.0510	0.0490	5.382260	0.000000
2459583	RF_maintenance	100.00%	95.08%	100.00%	0.00%	100.00%	0.00%	-0.303957	20.957743	3.658750	35.780314	1.966248	23.488721	-0.888912	0.844620	0.3135	0.0587	0.0715	5.461024	1.405559
2459582	RF_maintenance	100.00%	94.59%	100.00%	0.00%	100.00%	0.00%	-0.148067	18.201238	3.615710	32.514314	2.745518	28.435869	-0.967390	1.048339	0.2944	0.0485	0.0799	3.766968	1.229989
2459581	RF_maintenance	100.00%	92.43%	100.00%	0.00%	100.00%	0.00%	0.026761	25.188317	3.310765	36.705822	2.143265	23.011797	-0.809994	1.227384	0.3182	0.0589	0.0544	3.046968	1.246641
2459580	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459579	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459578	RF_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	1.340812	1.438385	1.006581	2.865572	0.941502	0.831967	-0.705475	3.926739	0.0360	0.0301	0.0007	nan	nan
2459577	RF_maintenance	100.00%	93.07%	100.00%	0.00%	100.00%	0.00%	0.466197	24.480037	3.220589	34.323290	2.749969	24.791726	-0.920984	1.216023	0.3169	0.0601	0.0694	3.352040	1.214785
2459576	RF_maintenance	100.00%	91.84%	100.00%	0.00%	100.00%	0.00%	0.329108	18.871051	2.456270	27.538106	2.822380	21.946274	-0.337624	2.040385	0.3256	0.0632	0.0456	4.815720	1.259367
2459575	RF_maintenance	100.00%	48.08%	100.00%	0.00%	100.00%	0.00%	0.331009	19.523887	2.503466	27.305016	2.189648	16.793274	3.222458	5.573376	0.4315	0.0983	0.1278	2.305083	1.164061
2459574	RF_maintenance	100.00%	81.71%	100.00%	0.00%	100.00%	0.00%	0.171386	17.782314	2.149887	26.164488	3.214755	24.241158	0.870104	1.263947	0.3323	0.0556	0.0561	4.419349	1.208078
2459573	RF_maintenance	100.00%	92.48%	100.00%	0.00%	100.00%	0.00%	0.536069	18.362622	5.035280	44.994470	3.032602	28.241260	-0.860034	2.513749	0.3184	0.0574	0.0463	0.000000	0.000000
2459572	RF_maintenance	100.00%	92.43%	100.00%	0.00%	100.00%	0.00%	0.215185	18.573798	2.419898	26.322046	1.688610	17.946414	-0.621131	4.337574	0.3197	0.0618	0.0465	4.815802	1.274120
2459571	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	1.403526	1.139708	1.117825	2.953286	0.232758	0.949275	-0.715699	4.154911	0.0386	0.0308	0.0019	nan	nan
2459570	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	RF_maintenance	100.00%	43.29%	93.51%	0.00%	100.00%	0.00%	2.036172	22.167774	2.553921	34.108263	3.462118	10.160239	2.995315	8.374285	0.5496	0.1568	0.2022	6.589243	1.262607
2459566	RF_maintenance	100.00%	92.43%	100.00%	0.00%	100.00%	0.00%	0.530275	17.815873	3.294955	34.112303	1.880733	21.641836	-0.516266	0.420224	0.2970	0.0563	0.0686	1.684163	0.920396
2459565	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	-1.185954	-1.185954	-1.077553	-1.077553	-0.891166	-0.891166	-0.958079	-0.958079	nan	nan	nan	0.000000	0.000000
2459564	RF_maintenance	100.00%	90.80%	98.90%	0.00%	100.00%	0.00%	2.500365	21.775568	3.180290	35.963596	0.066914	2.076898	-1.692845	1.759181	0.3201	0.1277	0.0496	3.617811	1.273814
2459563	RF_maintenance	100.00%	92.92%	100.00%	0.00%	100.00%	0.00%	6.160380	22.192157	24.445879	68.698901	12.528063	21.872858	-1.931005	3.325832	0.2955	0.1070	0.0192	1.924553	1.203422
2459562	RF_maintenance	100.00%	90.86%	100.00%	0.00%	100.00%	0.00%	-0.195305	33.805525	-2.273125	28.176596	-0.155690	2.556145	-0.225258	9.047210	0.3291	0.0730	0.0577	3.863879	1.181509
2459561	RF_maintenance	100.00%	92.48%	100.00%	0.00%	100.00%	0.00%	1.954031	15.751053	1.674101	16.953678	0.067468	2.802683	-1.860657	3.050547	0.3152	0.1188	0.0320	3.892132	1.317862
2459560	RF_maintenance	100.00%	93.51%	100.00%	0.00%	100.00%	0.00%	1.497100	13.910582	1.269489	17.218601	0.140562	3.322698	-1.602512	2.153136	0.2776	0.0983	0.0413	3.591867	1.284764
2459559	RF_maintenance	100.00%	87.52%	100.00%	0.00%	100.00%	0.00%	2.359183	18.618826	1.868746	18.859163	0.633712	3.321290	-0.868596	2.652901	0.3148	0.1214	0.0600	2.715287	1.252984
2459558	RF_maintenance	100.00%	92.38%	100.00%	0.00%	100.00%	0.00%	6.254479	22.727163	13.224320	36.130095	13.573031	23.973614	-1.938359	3.767321	0.3030	0.1047	0.0566	2.199178	1.245358
2459557	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0429	0.0660	0.0029	nan	nan
2459556	RF_maintenance	100.00%	91.94%	100.00%	0.00%	100.00%	0.00%	6.671189	24.030743	13.793040	39.113979	12.468785	20.416958	-3.407793	8.246334	0.3217	0.1195	0.0523	2.166000	1.247190
2459554	RF_maintenance	-	92.38%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.2905	0.0997	0.0555	nan	nan
2459553	RF_maintenance	-	92.09%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.3071	0.1120	0.0524	nan	nan
2459552	RF_maintenance	100.00%	87.03%	100.00%	0.00%	100.00%	0.00%	7.652533	27.055167	17.935288	51.667591	19.132751	31.938624	0.723975	5.308105	0.3162	0.1034	0.0659	2.710913	1.054830
2459551	RF_maintenance	100.00%	91.84%	100.00%	0.00%	100.00%	0.00%	1.619190	14.766986	1.696583	20.910410	0.108656	3.877728	-1.841189	3.161419	0.3150	0.1230	0.0574	2.921627	1.266849
2459550	RF_maintenance	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0614	0.0588	-0.0002	nan	nan
2459549	RF_maintenance	100.00%	91.89%	100.00%	0.00%	100.00%	0.00%	5.839762	21.186043	13.272957	38.940836	13.647375	21.931005	-2.505335	3.886817	0.3079	0.1099	0.0411	1.936338	1.156240
2459542	RF_maintenance	100.00%	69.57%	100.00%	0.00%	100.00%	0.00%	6.830653	28.771981	26.370373	78.605080	0.480724	1.928727	-1.977264	2.972463	0.3996	0.1565	0.0948	1.994081	1.248259
2459541	RF_maintenance	100.00%	67.34%	86.58%	0.00%	100.00%	0.00%	6.043663	12.601784	16.454278	37.028047	9.911522	253.576829	4.526839	16.358309	0.4209	0.1926	0.0985	1.967970	1.276248
2459540	RF_maintenance	100.00%	69.13%	99.55%	0.00%	100.00%	0.00%	3.406562	19.899943	3.124907	17.967505	1.183488	5.129279	-0.855572	1.711168	0.4063	0.1499	0.1091	2.694656	1.275945
2459536	RF_maintenance	100.00%	95.63%	100.00%	0.00%	100.00%	0.00%	8.694580	36.030033	21.702185	67.013266	20.128416	30.971451	-3.742292	6.660403	0.2388	0.0944	0.0266	1.426062	0.864542
2459535	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.766344	16.052394	2.781036	15.717102	-0.425772	5.710425	-1.684152	1.583790	0.0816	0.0559	0.0161	1.176626	1.168650
2459534	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.582842	17.705310	1.487247	15.221849	-0.390198	3.759369	-1.169537	0.987861	0.1086	0.0595	0.0313	1.162035	1.146606
2459533	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.892706	25.989240	13.615660	36.525184	11.630310	20.073197	-2.667519	4.375764	0.0949	0.0583	0.0307	1.164085	1.149107
2459532	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.959327	30.180781	17.867022	49.509056	14.651649	24.632442	-2.434084	3.189384	0.0917	0.0565	0.0274	0.838928	0.831095
2459530	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.517310	15.443676	1.404030	15.489171	0.303869	3.919334	-1.423737	1.956386	0.1033	0.0601	0.0302	1.160803	1.150219
2459527	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	7.800524	35.859366	14.533434	55.034742	29.909745	32.105255	-1.310462	1.974458	0.6420	0.3565	0.0980	0.000000	0.000000
2459523	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	6.949775	30.008946	19.577295	52.865517	8.253930	27.936450	-5.517292	4.922156	nan	nan	nan	2.528906	1.069802
2459522	RF_maintenance	100.00%	54.55%	100.00%	0.00%	100.00%	0.00%	7.572891	30.279298	18.125143	53.579790	3.025568	22.423276	-3.211735	5.176314	0.3904	0.1367	0.0363	1.703795	0.896422
2459513	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	5.473629	1.527884	10.212558	3.345628	3.910963	1.311970	0.192335	6.405219	0.0520	0.0489	0.0023	nan	nan
2459508	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.532006	41.555065	11.713601	51.544335	12.857002	15.137524	10.819938	11.349162	0.1065	0.0864	0.0150	0.000000	0.000000
2459505	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	125.351324	102.884159	130.615341	106.662471	234.925624	111.231623	1224.690418	558.041826	0.0092	0.0072	0.0052	1.093665	1.099483

Antenna Season Report Notebook¶

Antenna 55 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 55, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 55: 2459626

Antenna 55: 2459625

Antenna 55: 2459624

Antenna 55: 2459623

Antenna 55: 2459622

Antenna 55: 2459621

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Antenna 55: 2459542

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Antenna 55: 2459536

Antenna 55: 2459535

Antenna 55: 2459534

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶